Who Are We?

RZR is an AI-native advertising platform built for the next era of performance marketing. We operate at the intersection of machine learning, programmatic media, and full-funnel mobile growth, powering campaigns for some of the world's most ambitious advertisers. Our platform is purpose-built to deliver outcomes at scale, not just impressions.

We are a team of builders, operators, and technologists who believe the advertising industry is overdue for a fundamental rethink. We move fast, operate with a high degree of ownership, and hold ourselves to an exceptionally high standard of craft.

RZR is scaling aggressively with an active M&A pipeline and a platform vision that puts us on a path to becoming an industry leader. This is a rare opportunity to join a company at an inflection point and help shape what it becomes.

Role Overview

As Machine Learning Engineer (Infra / MLOps) at RZR, you will design, build, and operate the model training and deployment infrastructure that powers our Demand-Side Platform (DSP). This role focuses on building scalable, flexible, and reliable systems for training models on billions of records across bidding, ranking, pacing, and fraud use cases.

You will work at the intersection of machine learning, data platforms, and infrastructure — with a strong focus on automation, reproducibility, and reliability. This is a P0 priority hire directly tied to accelerating RZR's migration from legacy model training systems to Prefect-based DNN pipelines, enabling 100% UA on DNN.

The right person for this role combines production-grade ML systems experience with a strong bias to automate, document, and build for reliability — someone who takes end-to-end ownership from data to serving, and is energized by the complexity of high-QPS real-time bidding infrastructure.

Key Responsibilities

Own the development and evolution of infrastructure that enables faster, more reliable, and more cost-efficient model training
Design, build, and maintain automated model training and orchestration pipelines that scale across large datasets and support rapid recovery from failures
Develop standardized training workflows that support experimentation, reproducibility, versioning, and traceability
Build and operate observability and monitoring systems to detect data quality issues, training instabilities, model anomalies, and performance regressions
Improve the efficiency, scalability, and maintainability of the model training codebase, defining and enforcing best practices across the ML organization
Apply DevOps and MLOps best practices to machine learning training workflows, including CI/CD and automated testing
Design, develop, and continuously optimize ML infrastructure for advertising recommendation systems, covering model training, online inference, model serving, and feature pipelines
Build a high-performance, highly scalable ML platform to support rapid iteration and stable deployment of advertising recommendation models
Optimize distributed training, online inference, and resource scheduling to continuously improve system performance, stability, and resource utilization
Collaborate closely with algorithm engineers to drive efficient implementation of recommendation, ranking, and ad-serving models
Stay current with advancements in ML infrastructure and AI technologies, including the application of LLMs in recommendation and advertising scenarios

Required Skills and Experience

Must-Have

Strong proficiency in Python and Spark for ML training and deployment workflows
Experience building and operating machine learning pipelines in production environments
Hands-on experience with DevOps practices including CI/CD, infrastructure as code, and automated testing
Experience with workflow orchestration tools such as Airflow or Prefect for ML pipelines
Solid understanding of ML experimentation, reproducibility, model versioning, and dataset management
Experience with large-scale data pipelines, feature generation, and offline/online data consistency
Experience developing recommendation systems, advertising systems, search systems, or machine learning platforms